Human-Inspired Flow-Crossing Navigation for Nonholonomic Mobile Robots in Dynamic Pedestrian Environments

This paper proposes a human-inspired navigation method that enables nonholonomic mobile robots to traverse dynamic pedestrian flows safely and efficiently. Conventional methods primarily focus on static environments or avoiding individual pedestrians; however, limited attempts have been made to validate robot crossing behavior experimentally within actual crowd scenarios. Therefore, this study implemented crossing navigation based on observed human behavior, specifically positioning robots behind pedestrians during crossing maneuvers. The algorithm developed herein identifies the optimal crossing points within dynamic pedestrian flows, considering robot nonholonomic constraints to ensure safety and efficiency. Additionally, potential field methods generate robot trajectories toward the identified crossing points, and pure pursuit control facilitates smooth trajectory tracking. Multiple simulations confirmed significant reductions in arrival time and path length compared with the conventional social-force-based method under uniform and nonuniform pedestrian flow conditions. Furthermore, pedestrian disturbances decreased, stabilizing the average walking velocities. In real-world experiments conducted in a 6 m × 6 m environment, robots successfully traversed pedestrian gaps without disrupting pedestrian flow. Moreover, pedestrians voluntarily yielded paths to the robot, indicating the importance of incorporating human social behaviors into robotic navigation planning. Thus, multiple simulation and experimental results demonstrated that the proposed method effectively balances safety and efficiency in robotic path planning through crowds.